Even basic tasks such as feeding ourselves, manipulating tools, and performing activities of daily living require some skill. Using specialized tools, for example by machinists such as welders, musicians, chefs and surgeons requires even more skill acquired through motor learning which requires extended practice. One main event that can impact the ability to perform these tasks is stroke.
There are over 8 million stroke survivors in the US. Majority of them do not have access to rehabilitation and have persistent hand dysfunction leading to chronic disability. Recovery of hand function after neurological injury such as stroke, cerebral palsy, multiple sclerosis, spinal cord injury etc. is extremely challenging. Recovery occurs through motor re-learning during which specific sensory-motor associations are formed to shape hand posture to match that of the object, and scale fingertip forces to the weight and texture of objects. These associations need to be fine-tuned through practice and established in long-term procedural memory to regain skill. However forming such task-specific memory requires flexible interaction with various types of objects in a systematic manner, appropriately rewarded for accuracy that can be repeated without becoming tiresome. Furthermore, it is challenging to facilitate the formation of specific sensory-motor associations because individuals tend to use compensatory strategies, such as increasing the abduction angle at the shoulder, and excessively co-activating the flexor and extensor muscles across a joint when attempting to complete the task. These compensatory strategies reinforce abnormal movements which makes it more difficult to regain skill in the long term.
Hemiparesis is the most common impairment after stroke and typically affects the upper extremity more than the lower extremity. Studies indicate that upper-extremity weakness, spasticity, and abnormal motor synergies are insufficient to explain the impairment in reaching movements after stroke (Twitchell, 1959; Wing et al., 1990; Roby-Brami et al., 1997), and suggest that additional higher-order control deficits may be present (Beer et al., 1999).
A well-characterized paradigm for the study of higher order sensorimotor integration in hand motor control is to measure subject's ability to anticipate the fingertip forces required to grasp and lift objects (Johansson, 1996). Anticipatory (feed-forward) fingertip force control ensures the generation of appropriate grip and load forces so as to avoid crushing delicate objects or dropping heavy ones, and is thought to be based on the formation of internal models of object properties in the central nervous system (Johansson and Westling, 1988; Gordon et al., 1993; Flanagan, 1999; Davidson and Wolpert, 2004). Anticipatory control of grasp is reflected in the ability to scale peak grip force rates (GFR) and peak load force rates (LFR) to the texture and weight of objects before confirmatory feedback becomes available (Johansson and Westling, 1988; Flanagan et al., 2001). Healthy subjects are able to appropriately scale peak force rates to object properties after just one or two lifts, and accurately recall those 24 hours later (Gordon et al., 1993; Flanagan et al., 2001).
Planning of precision grasp was assessed by measurement of anticipatory scaling of peak LFR and peak GFR to object weight, as the peak amplitude of these variables is scaled to the expected weight of the object before sensory feedback signaling the object's weight is available at lift-off (Johansson and Westling, 1988; Gordon et al., 1993; Flanagan et al., 2001). Scaling of the peak force rate ensures that the time to produce lifting forces does not increase linearly with object weight. Precision grasp execution was assessed by measurement of the timing and efficiency of grip-load force coordination, as these variables indicate the degree of fine motor control necessary for precision grasp (Forssberg et al., 1999). Transfer paradigms are likely to give us a better understanding of how information is exchanged between the two hemispheres and may have important implications for the development of rehabilitation strategies that incorporate practice with the non-involved hand prior to practice with the involved hand to improve grasping behavior after stroke (Raghavan et al, 2006).
The ability to predict and anticipate the mechanical demands of the environment promotes smooth and skillful motor actions. Thus, the finger forces produced to grasp and lift an object are scaled to the physical properties such as weight. Information about the relevant object properties can also be inferred from visual cues. A particularly important cue is the size of the object, which enables an estimation of the weight when the material is known. It has been frequently demonstrated that grip and load forces indeed anticipate object size (Gordon et al. 1991a, b; Cole 2008; Li et al. 2009). In addition to size, other physical object characteristics determine the grip force necessary to hold an object. Thus, friction at the finger-object contact is crucial and it has been shown that changes in the objects surface material with altering friction are precisely anticipated on the basis of the last lifting trial (Cadoret and Smith 1996; Flanagan and Johansson 2002; Johansson and Westling 1984).
Motor learning has been shown to occur over multiple time-scales. At least three underlying processes are thought to contribute to learning: (1) error-based adaptation (fast process), (2) repetition that alters movement biases depending on what is repeated (slow process), and (3) reinforcement that occurs when error is reduced successfully and leads to savings or faster re-learning on subsequent attempts. Currently available interactive platforms do not facilitate real-time interaction with kinesthetic and haptic feedback in a controlled and paced manner for rehabilitation. There is a need for systems and methods to enhance motor re-learning for restoration of hand function, especially after stroke. In particular, there is a need for a low cost commercial device that can measure grip and load forces applied by the subjects to measure dexterity. There is also a need for systems and method of statistical analysis for interpreting clinical data from such devices for the purpose of diagnosis of the extent of hand dysfunction, prognosticate improvement with specific types of therapy and to provide feedback and metrics on degree of improvement.